Peptides are important regulatory molecules involved in a variety of biological mechanisms. Their function is generally determined by processing kinetics, interaction specificity and, more fundamentally, binding affinity. A thorough understanding of the contributions relevant for stable complex formation may form the basis of experimental rationalization, detection of novel ligands and optimization of lead compounds. Predictive structure-based methods can be very helpful, provided that they are of sufficiently high accuracy.
Structure-based binding studies face two major technical barriers. The first resides in the prediction of accurate 3D-structures for peptide/receptor complexes. Peptides are conformationally very flexible since most of their chemical bonds are subject to free rotation. A partial solution is to perform flexible docking using predefined rotamers. Yet, deviation from ideal rotameric states and small-scale flexibility due to bond angle bending present additional difficulties. Occasionally, peptides adopt variable binding modes, partly bulge out into solvent or let some flanking residues hang out of the binding site. Yet, it is often observed that one or more peptide side-chains are anchored into well-shaped pockets in the interface surface. In such cases, conformational flexibility is limited, which facilitates structure-based analysis.
The second problem is how to derive accurate binding affinities from experimental or modeled representations. Even short peptides easily contain more than a hundred atoms, making thousands of small pairwise atomic interactions. Further, ligand/receptor interfaces are rarely optimally packed and can include multiple water molecules. Finally, binding affinity depends on thermodynamic properties of the bound and free states of the molecules involved.
In view of these complications, structure-based affinity scoring methods invariably include approximations and/or a parameterization step wherein physically relevant effects are captured into tunable parameters. A distinction should be made between statistical and empirical methods. Statistical, or knowledge-based scoring methods operate on the basis of atom (or group) contact potentials derived from known protein structures. Empirical, or partitioning methods work with predefined physical energy terms, represented by parameterized mathematical equations that are optimized against experimental data. In view of the inevitable training step, validation on independent data is required. Here, it is not uncommon that methods performing relatively well on data similar to the training set are significantly less accurate on more divergent datasets or must even be retrained. Transferability therefore remains an important and delicate matter. Transferability is defined herein as the use of one and the same scoring function for different receptor/ligand systems.
The present invention is related to the binding characteristics of anchor residues within peptide ligands of receptor molecules, e.g. human leukocyte antigen (HLA) complexes. HLA class I molecules are immunologically important receptors involved in specific recognition between cytotoxic T lymphocytes and pathogen-infected cells. Pathogen-derived peptides, known as antigens, are mostly 8-10 residues long. Structural information from the Protein Data Bank (PDB) is available for an increasing number of receptor subtypes (at present about ten). Common features of these complexes are the strong interactions between receptor side-chains and the N- and C-terminal ends of the peptide backbone. The side-chains of peptide residues P2 (or, occasionally, P3) and P9 (for 9-mers) are located into well-formed pockets named B (or D) and F, respectively. Hence, the side-chain orientation of anchor residues and their structural context are relatively fixed. Yet, there is a significant variety in anchor properties among different subtypes. Finally, anchor residues are dominant contributors to total affinity and greatly determine binding specificity. For all these reasons, anchor residues in HLA class I complexes are ideally suited to develop or test novel affinity scoring methods.
The present invention relates to the identification of the physico-chemically most relevant affinity determinants. Possible contributions like contact-based potentials, weight-adapted conformational energy terms, shape complementarity, hydrophobic corrections and different entropical components may yield good results but poor transferability. Because of the danger of overparameterization, erroneous assignment of either false or redundant contributions is likely. Underparameterization, in particular of conformational strain, is another problem. The inventors therefore examined the possibility to develop a scoring function based exclusively on an established force field function. The principal advantage of force field based approaches is that different physico-chemical interactions can be computed in a consistent way.